Search Results (4,990)

Background/Objectives: Malnutrition is an imbalance of nutrients required for growth, development, and organ function. Its impact on bone development is known, but its effects on cartilage remain unclear. This study aimed to evaluate the femoral cartilage thickness in children with primary malnutrition. Methods: In this cross-sectional observational study, 83 children with primary malnutrition and 62 age- and sex-matched healthy controls were included. Patients with primary malnutrition were classified as mild, moderate and severe. Femoral cartilage thickness measurements of all children were taken by ultrasound from the femoral lateral condyle, femoral medial condyle and intercondylar area for both knees with the patient in a supine position with the knees flexed 90 degrees. Results: The right lateral, right medial, left lateral, and left medial femoral cartilages were significantly thicker in patients with malnutrition compared to those without malnutrition (p = 0.002, 0.004, <0.001, and 0.001, respectively). A significant negative correlation was found between age, weight Z-score, and height Z-score and triceps skinfold thickness. Conclusions: Distal femoral cartilage thickness is significantly greater in children with primary malnutrition. This demonstrates the effect of nutritional factors on cartilage tissue and suggests that children with chronic malnutrition are at risk for both knee joint problems and short stature later in life. Full article

This paper examines the wettability and interactions between ceramic and composite materials soldered with Bi-based solder containing 11 wt.% of silver and 3 wt.% titanium using indirect electron beam soldering technology. The Bi11Ag3Ti solder, with a melting point of 402 °C, consisted of a bismuth matrix containing silver lamellae. Titanium, acting as an active element, positively influenced the interaction between the solder and the joined materials. SiC and Ni-SiC substrates were soldered at temperatures of 750 °C, 850 °C, and 950 °C. Measurements of wettability angles indicated that the lowest value (20°) was achieved with SiC substrates at 950 °C. A temperature of 750 °C appeared to be the least suitable for both substrates and was entirely unsuitable for Ni-SiC. It was also observed that the Bi11Ag3Ti solder wetted the SiC substrates more effectively than Ni-SiC substrates. The optimal working temperature for this solder was determined to be 950 °C. The shear strength of the joints soldered with the Bi11Ag3Ti alloy was 23.5 MPa for the Al₂O₃/Ni-SiC joint and 9 MPa for the SiC/Ni-SiC joint. Full article

The influence of soft tissue structures, including ligaments spanning one or more intervertebral junctions and the nuchal ligament, on motion of the equine cervical joints remains unclear. The present study addressed this using four post-mortem horse specimens extending from head to withers with all ligaments intact. Three-dimensional kinematics was obtained from markers on the head and bone-anchored markers on each cervical and the first thoracic vertebra during rotation, lateral bending, flexion and extension of the whole head, and neck segment. Yaw, pitch, and roll angles in 8 cervical joints (total 32) were calculated. Flexion and extension were expressed mainly as pitch in 27 and 22 joints, respectively. Rotation appeared as predominantly roll in 13 joints, whereas lateral bending was represented as predominantly yaw in 1 and as roll or pitch in all other joints. Significant correlations between yaw, pitch, and roll were observed at individual cervical joints in 97% of all measurements, with the atlanto-occipital joint showing complete (100%) correlation. Most non-significant correlations occurred at the C5–C6 joint, while C6–C7 exhibited significantly lower correlation coefficients compared to other levels. The overall movement of the head and neck is not replicated at individual cervical joint levels and should be considered when evaluating equine necks in vivo. Full article

Background: Residual deficits after early treatment of developmental dysplasia of the hip (DDH) using osteotomy often led to asymmetrical gait deviations with increased repetitive rates of ground reaction force (GRF) in both hips, resulting in a higher risk of early osteoarthritis. This study investigated lower limb inter-joint coordination and swing foot control during level walking in adolescents with early-treated unilateral DDH. Methods: Eleven female adolescents treated early for DDH using Pemberton osteotomy were compared with 11 age-matched healthy controls. The joint angles and angular velocities of the hip, knee, and ankle were measured, and the corresponding phase angles and continuous relative phase (CRP) for hip–knee and knee–ankle coordination were obtained. The variability of inter-joint coordination was quantified using the deviation phase values obtained as the time-averaged standard deviations of the CRP curves over multiple trials. Results: The DDH group exhibited a flexed posture with increased variability in knee–ankle coordination of the affected limb throughout the gait cycle compared to the control group. In contrast, the unaffected limb compensated for the kinematic alterations of the affected limb with reduced peak angular velocities but increased knee–ankle CRP over double-limb support and trajectory variability over the swing phase. Conclusions: The identified changes in inter-joint coordination in adolescents with early treated DDH provide a plausible explanation for the previously reported increased GRF loading rates in the unaffected limb, a risk factor of premature OA. Full article

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity. Full article

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

Cranial cruciate ligament (CrCL) rupture is a common orthopedic disorder in dogs, leading to stifle joint instability and progressive osteoarthritis. This study aimed to develop and biomechanically evaluate a novel intra-articular reconstruction system designed to mimic the natural ligament and restore joint stability following CrCL excision. The system consisted of a 3D-printed thermoplastic polyurethane (TPU) graft, cerclage wire, and H-button fixation. Fourteen pelvic limbs from mature mixed-breed cadaveric dogs were used. The inclination angle, dimensions, volume, tensile strength, and elongation of the native CrCL were measured. Seven CrCL-deficient stifles were reconstructed using the proposed system and tested biomechanically. The native CrCL showed a significantly higher tensile strength than the TPU graft; however, the TPU demonstrated a greater flexibility. The reconstruction system successfully stabilized the joint and provided repeatable fixation. Significant correlations were found between CrCL volume and both age and body weight. These findings support the mechanical suitability of the proposed system for ex vivo stifle stabilization and highlight the potential of 3D-printed TPU in ligament reconstruction. Further in vivo studies are recommended to assess long-term performance, including implant integration, tissue remodeling, and clinical outcomes. Full article

There is limited knowledge about the benefits of functional textile in arthritis management. This study aimed to evaluate the effect of wearing functional socks in patients with rheumatoid or psoriatic arthritis. Patients were randomized into an experimental group (n = 23) and control group (n = 18). The intervention involved wearing functional textile socks for 12 weeks. Sock composition was analyzed using X-ray fluorescence spectrometry and scanning electron microscopy. Outcome measures included the Numeric Rating Scale, Health Assessment Questionnaire–Disability Index (HAQ-DI), and RAND-36 (Estonian version). At week 12, the experimental group showed significantly lower metatarsophalangeal and toe joint pain (p = 0.001), stiffness (p = 0.005), and ankle stiffness (p = 0.017) scores than the control group. Improvements were also observed in HAQ-DI reaching (p = 0.035) and activity (p = 0.028) scores. RAND-36 scores were higher in physical functioning (p = 0.013), social functioning (p = 0.024), and bodily pain (p = 0.006). Role limitations due to physical problems improved in the experimental group but worsened in the control group (p = 0.029). In conclusion, wearing functional socks led to some statistically significant improvements in foot and ankle pain and stiffness, physical function, and health-related quality of life. However, the effect sizes were small, and the clinical relevance of these findings should be interpreted with caution. Full article

Periprosthetic joint infections (PJIs) represent one of the most problematic complications following total joint replacement, with a significant impact on the patient’s quality of life and healthcare costs. The early and accurate diagnosis of a PJI remains the key factor in the management of such cases. However, with traditional diagnostic measures and risk assessment tools, the early identification of a PJI may not always be adequate. Artificial intelligence (AI) algorithms have been integrated in most technological domains, with recent integration into healthcare, providing promising applications due to their capability of analyzing vast and complex datasets. With the development and implementation of AI algorithms, the assessment of risk factors and the prediction of certain complications have become more efficient. This review aims to not only provide an overview of the current use of AI in predicting PJIs, the exploration of the types of algorithms used, and the performance metrics reported, but also the limitations and challenges that come with implementing such tools in clinical practice. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

Temporomandibular disorders (TMDs) encompass a heterogeneous group of musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ) and masticatory system. Due to academic stress and parafunctional habits, dental students may be particularly vulnerable to TMD. Objective: To determine the prevalence of TMD symptoms and their psychosocial and functional correlates among students at the Faculty of Dental Medicine, UMPh Iasi, Romania, using the diagnostic criteria for TMD (DC/TMD) self-report axis and axis II instruments. Methods: In this cross-sectional survey, 356 volunteer students (66.0% female; mean age, 22.9 ± 3.6 years) out of a total population of 1874 completed an online DC/TMD–based questionnaire. Axis I assessed orofacial pain, joint noises, and mandibular locking. Axis II instruments included the Graded Chronic Pain Scale (GCPS), Jaw Functional Limitation Scale (JFLS-20), Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Oral Behaviors Checklist (OBC). Descriptive statistics summarized frequencies, means, and standard deviations; χ² tests and t-tests compared subgroups by sex; Pearson correlations explored relationships among continuous measures (α = 0.05). Results: A total of 5% of respondents reported orofacial pain in the past 30 days; 41.6% observed TMJ noises; 19.7% experienced locking episodes. Mean JFLS score was 28.3 ± 30.5, with 4.8% scoring > 80 (severe limitation). Mean PHQ-9 was 5.96 ± 5.37 (mild depression); 15.5% scored ≥ 10. Mean GAD-7 was 5.20 ± 4.95 (mild anxiety); 16.0% scored ≥ 10. Mean OBC score was 12.3 ± 8.5; 30.1% scored ≥ 16, indicating frequent parafunctional habits. Symptom prevalence was similar by sex, except temporal headache (43.4% females vs. 24.3% males; p = 0.0008). Females reported higher mean scores for pain intensity (2.09 vs. 1.55; p = 0.0013), JFLS (32.5 vs. 18.0; p < 0.001), PHQ-9 (6.43 vs. 5.16; p = 0.048), and OBC (13.9 vs. 9.7; p = 0.0014). Strong correlation was observed between PHQ-9 and GAD-7 (r = 0.74; p < 0.001); moderate correlations were observed between pain intensity and PHQ-9 (r = 0.31) or GAD-7 (r = 0.30), between JFLS and pain intensity (r = 0.33), and between OBC and PHQ-9 (r = 0.39) (all p < 0.001). Conclusions: Nearly half of dental students reported TMD symptoms, with appreciable functional limitation and psychosocial impact. Parafunctional behaviors and psychological distress were significantly associated with pain and dysfunction. These findings underscore the need for early screening, stress-management interventions, and interdisciplinary care strategies in the dental student population. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

Three-dimensional (3D) radar imaging is essential for target detection and measurement of scattering characteristics. Cylindrical scanning, a prevalent spatial sampling technique, provides benefits in engineering applications and has been extensively utilized for assessing the radar stealth capabilities of large aircraft. Traditional cylindrical scanning generally utilizes highly sampled full-coverage techniques, leading to an excessive quantity of sampling points and diminished image efficiency, constraining its use for quick detection applications. This work presents an efficient 3D sampling strategy that integrates vertical linear scanning with horizontal rotating motion to overcome these restrictions. A joint angle–space sampling model is developed, and geometric constraints are implemented to enhance the scanning trajectory. The experimental results demonstrate that, compared to conventional techniques, the proposed method achieves a 94% reduction in the scanning duration while maintaining a peak sidelobe level ratio (PSLR) of 12 dB. Furthermore, this study demonstrates that 3D imaging may be accomplished solely by a “V”-shaped trajectory, efficiently determining the minimal possible sampling aperture. This approach offers novel insights and theoretical backing for the advancement of high-efficiency, low-redundancy 3D radar imaging systems. Full article

There is a long tradition of studying river sharing problems. A central question frequently examined and addressed is how common benefits or costs can be distributed fairly. In this context, axiomatic approaches of cooperative game theory often use contradictory principles of international water law, which are strictly rejected in practice. That leads to the question: Are these methods suitable for a real-world application? First, we conduct a systematic literature review based on the PRISMA approach to categorise the river sharing problems. We identified several articles describing a variety of methods and real-world applications, highlighting interdisciplinary interest. Second, we evaluate the identified axiomatic literature related to TU games with regard to their suitability for real-world applications. We exclude those “standalone” methods that exclusively follow extreme principles and/or do not describe cooperative behaviour. This is essential for a fair distribution. Third, we propose to use the traditional game-theoretical approach of airport games in the context of river protection measures to ensure a better economic interpretation and to enforce future cooperation in the joint implementation of protective measures. Full article

This paper investigates the problem of tracking targets with unknown but fixed 3D star-convex shapes using point cloud measurements. While existing methods typically model shape parameters as random variables evolving according to predefined prior models, this evolution process is often unknown in practice. We propose a particular approach within the Expectation Conditional Maximization (ECM) framework that circumvents this limitation by treating shape-defining quantities as parameters estimated directly via optimization. The objective is the joint estimation of target kinematics, extent, and orientation in 3D space. Specifically, the 3D shape is modeled using a radial function estimated via double Fourier series (DFS) expansion, and orientation is represented using the compact, singularity-free axis-angle method. The ECM algorithm facilitates this joint estimation: an Unscented Kalman Smoother infers kinematics in the E-step, while the M-step estimates DFS shape parameters and rotation angles by minimizing regularized cost functions, promoting robustness and smoothness. The effectiveness of the proposed algorithm is substantiated through two experimental evaluations. Full article